When seeking information, many people rely upon sources such as the internet, intranets, pamphlets, magazines, and advertisements to provide them with adequate information and ultimately to aid in their decision-making process. In their searches, however, such sources often include barriers that prevent people from acquiring the valid, reliable and useful information they need. Notably, the anonymity of interconnected computer networks (e.g., the internet) prevents people from trusting the reliability of the information source. Clearly, most people would rather consult their friends and colleagues that they know and trust on a first name basis—or knowledgeable people that they know through their friends and colleagues—when seeking the answer to a particular question. For example, it is well known that informal communication via personal communication networks allows decision makers to reduce the uncertainty regarding unfamiliar technologies and/or products by questioning and consulting trusted others. Posing questions to the members of one's personal communication network allows individuals to obtain first, second, and third-hand accounts from individuals they know directly or through intermediaries. Theoretically, the varied experiences of one's network of peers, acquaintances, and people connected to the person through countless others should more than adequately serve to answer one's questions. Unfortunately, experiential and other knowledge can be difficult to procure; because people are unaware of who in their interpersonal network has experience or information regarding the information they seek, informal searches for advice can seem arbitrary, unfocused, and inefficient. The absence of a formal map or knowledge of communication structure prevents the person from realizing the full potential of the collective IQ of his network of friends and colleagues.
Social network analysis is known and has been described as the mapping and measuring of relationships and flows between people, groups, organizations, computers, or other information knowledge processing entities.
Social network analysis (SNA) can be used to generate data and draw conclusions based upon the flow of information (or other resources) within a social network. SNA maps the relationships of people within a social network in order to monitor, understand, and utilize the informational flow within the network—who do people get their information from and who do they give it to? A social network is distinct from an organizational chart because the organizational chart shows formal relationships—who works where and who reports to whom. On the other hand, a social-network-analysis map shows more informal relationships—who knows who and who do they share information with. SNA therefore facilitates visualizing and understanding personal relationships that can either facilitate or impede knowledge creation and sharing.
While social network analysis is known, little has been done to streamline its use in an effort to maximize its potential. Further, implementations of social-network analysis have yet to be fully explored. Specifically, most individuals interested in social network data have merely conducted interviews or surveys to obtain the data, and they have then kept the conclusions drawn from such data exclusively in the world of academia. For example, sociologists who studied the diffusion of hybrid seeds through the social networks of farmers in Iowa published their findings in academic journals. They did not, however, disclose the conclusions that they reached based on the analysis of their data to the general public.
SNA is gaining popularity in the field of marketing in order to facilitate the diffusion of innovations (e.g., new products) through customer networks. To this end, a number of companies have conducted preliminary data analyses using SNA in an attempt to map customer networks and determine who most customers contact for advice within a particular domain. In theory, if a company can identify and market to the small percentage of people that make up the opinion leadership or opinion leaders within a given customer network, they can lower both the cost of marketing and the time it takes for the innovation to diffuse through the customer network. Marketing departments are therefore anxious to identify “opinion leaders” within a given field. Such individuals are often highly connected “hubs” within a social network web, and they are important targets for marketing because other members in the customer network often go to them for advice regarding the latest trends and innovations. Clearly, the ability to selectively target opinion leaders, which may cut advertising and marketing costs while simultaneously increasing the effectiveness of marketing messages, would be highly beneficial. However with current technology, collecting, mapping, and identifying what role each potential customer plays within a given network demands considerable time, effort, and money-making such an approach prohibitive to all but a few companies.
While companies first demonstrated interest in the utility of SNA for targeted marketing in the 1950's, prior-art technology is slow and cumbersome. Most recently in the pharmaceutical domain, some pharmaceutical companies gathered relational information within the medical field by sending a two-page survey to approximately 800,000 physicians in the United States. The pharmaceutical companies paid each participating physician approximately $250 for their time, but the survey yielded only a 5% to 8% response rate—this equates to a one time $10,000,000 to $16,000,000 data-collection procedure. Further limitations on the accuracy or utility of such a strategy include the “static” nature of a one-time survey that fails to capture the dynamic nature of social networks.
Additional prior-art methods for performing SNA exist. One prior art method attempts to draw an inference on who is well known and influential within the field of medicine based on general publications, conference presentations and disclosures. This prior-art method is clearly limited in its lack of a social-network map that clearly depicts the informal and formal communication links between physicians. In other words, the approach is lacking because the data does not directly and clearly correspond to advice, influence, or communication among physicians. Clearly, a new approach to the collection of reliable, valid, meaningful, and cost-effective social-network data is needed.
In the domains of leisure and entertainment, parlor games such as “Six Degrees of Kevin Bacon” and websites such as “Friendster” and “LinkedIn” have demonstrated the ability of an internet system to create social networks of friends and business associates for the purposes of making friends, finding dates, identifying potential job candidates, and seeking employment. A major drawback of such popular social-network sites, however, is the seemingly arbitrariness of the links between users. Allowing “friends” to link to one another in a situation that almost promotes competition to score high volumes of links creates a chaotic environment wherein the context, strength, or value of relationships between users cannot be ascertained. Arbitrary links undermine the utility of social networks that purport to connect people to trustworthy second and third-degree contacts premised upon mutual “friends.” Therefore, the data captured and utilized by these websites is highly unreliable. Because the websites have not set parameters, guidelines, or norms to govern or define the links between users, the social networks generated by these sites provide limited aid to users and are nearly useless to parties interested in using social-network data for their own purposes.
Previous methods for inviting new people into social networks online or indicating first-degree contacts via a survey typically lack the sophistication to accurately capture the directionality of an established social-network link. It is generally known that social-network links can be either unidirectional (e.g., from A to B) or bi-directional (e.g., from A to B and from B to A). Capturing reliable, valid, and meaningful social-network data typically necessitates the directionality of the links within a social-network. Establishing and recording accurate directionality information about social-network links increases both the meaning and utility of a social-network map and social-network data generated therefrom. Prior-art methods for inviting (or listing) people into a social network often erroneously or prematurely infer bidirectional relationships—and misinterpretation of the directionality of a link leads to misleading information.
More specifically, prior-art methods directed to determining the directionality of social-network links do not provide a way to confirm the actual existence of a unidirectional or bi-directional link. For example, in the prior art, a first person will typically declare that a second person is linked to the first person, and as a result, the second person is incorporated into the first person's social network as a unidirectional or bi-directional link. Note that the prior-art methods don't provide for a way to confirm the existence or directionality of the link. In other words, the prior art doesn't provide for a method by which the second person can confirm or deny the relationship that the first person has alleged. Further, if a first person listed a second person as a member of the first person's social network, then the prior art doesn't provide a way to consult the second person as a way to confirm the relationship. The art therefore needs a more accurate method for determining the directionality of a social-network link.
Earlier internet search engines are typically designed to match search criteria—general words, names, phrases, etc.—with a list of “best fit” websites, based upon keywords and the popularity of the websites. The recent application of social networks to such search engines has introduced the concept of including evaluation of websites by an individual's contacts in the ranked presentation of the “best fit” websites. There still, however, remains a need in the art for an electronic search engine that can both: identify individuals in a field of interest that have knowledge regarding the searched topic and how the searcher is connected through a set of intermediaries to the individual that possesses the knowledge, and allows the searcher to ascertain the degree to which the person and information can be trusted.
Prior-art methods for performing broadcast searches of data are well known. And broadcast searching is widely used in many areas of technology today. Broadcast searching can generally be described as a search method that searches all available searchable data in an effort to locate the sought-after data. Broadcast searching can be slow and cumbersome, and there is therefore a need in the art for an additional search method for searching collections of databases and social networks.